Suppression of ringing artifacts during image resizing

ABSTRACT

An economical method of detecting and suppressing ringing artifacts during digital image resizing is presented. The economical method substitutes costly division calculations with cost effective comparator operations. The method also includes improved image sharpening when up-sampling an image.

BACKGROUND OF THE INVENTION

This invention relates to image resizing. More particularly, thisinvention relates to image resizing in which ringing artifacts aresuppressed.

An image is a stored description of a graphic picture (e.g., video,text, etc.) and is often described as a set of pixels having brightnessand color values. A pixel (i.e., a picture element) is generally onespot in a grid of spots that form the image.

Resizing an image involves an alteration in the pixel representation ofthat image. For example, to reduce the size of an image, fewer pixelsare used. To enlarge the size of an image, more pixels are used. Imageresizing may also include altering pixel brightness and color values.Resizing a digital representation of an image may be accomplished usinga digital filter to change the sampling density of the image. Thus,resizing and “re-sampling” can be considered the same process.

A signal can be more efficiently re-sampled in the digital domain ratherthan in the analog domain, which involves converting the digitalrepresentation of an image into the analog domain, filtering, and thenconverting back to the digital domain. An input sampling grid can beused to describe the positions of the samples or pixels. It is commonlyassumed that {0,0} is the {x,y} coordinate of the top left pixel of animage. An increase in an x-axis value indicates a move to the right inthe image, while an increase in a y-axis value indicates a move down theimage. While other coordinate systems may also be valid, historically,this system follows the standard scan order of a television screen,computer monitor, or other comparable display. A particular output pixelcan be generated at a certain position in the input grid. For example,position {5.37,11.04} means 37/100 of the distance from column 5 (left)to column 6 (right) in the x-axis, and 4/100 of the distance from row 11(above) to row 12 (below) in the y-axis. The position is said to have aninput pixel index part (i.e., an integer index less than or equal to5.37 (e.g., 5)), and a fractional part (e.g., 37/100) corresponding tothe fraction of the distance from the integer to the next pixel indexalong an axis.

Re-sampling two-dimensional image data can often be simplified byperforming axis-separable processing. In other words, re-sampling canoccur along each axis independently of the other. If high qualityre-sampling is performed along each axis, then the overall re-samplingquality after combining processing from both axes should also be of highquality. In hardware, each re-sampling implementation can be based onprocessing pipelines, so a separate re-sampling system can be used foreach axis for better performance. In software, one subroutine may beable to serve both axes.

As is well known in the art, ringing artifacts may occur in linearfiltering operations that attempt to maintain frequency response up to afinite level. Such a filter applied to an edge causes ringing neareither side of the edge on the output. In both cases, ringing artifactsare visible on a filtered image as an intensity rippling. Intensityrippling is a variation in the intensity of a displayed image as afunction of the distance from the feature causing the ringing. Intensityrippling is most visible around features such as impulses or transitions(i.e., steps from one level to another). Impulses can be described as ajump or spike with respect to neighboring input samples where the widthof the spike is extremely narrow.

The requirements for re-sampling of text and graphics images isdifferent than for video images. In the former case, sharp edges and theabsence of ringing artifacts on transitions are important.

In contrast, for some images, the preservation of spectral content insome regions is important. For other regions, behavior closer to that ofgraphics and text is ideal. For example, an image may consist of singlesine-wave components at each position with uniform and smooth frequencychanges as a function of position (such as found in typical “zone-plate”test patterns, which are video test signals in which the spatialfrequencies are a smooth function of {x,y} position). Spectralpreservation only works when re-sampling such images. In contrast,transitions between objects are not spectral in nature, and a text-likeinterpretation may be more appropriate.

When digitally processing an image, it is often desirable to be able toreduce the size of images without introducing artifacts in the reducedimage. Artifacts are image content that visibly alter the appearance ofthe original image. Artifacts can be present in a variety of imagetypes. Ideally, a pleasing image appearance should be maintained whenreducing an image (i.e., decimating the pixel data), even though someartifacts may still occur.

Decimation filters are used for image reduction and are usually designedfrom a spectral perspective. However, filters designed spectrallyrequire many taps that are very expensive to implement. Moreover,filters with negative coefficients are obtained, which may produceringing artifacts on sharper transitions. As is well known in the art,better spectral re-sampling is possible when more input points are usedto create each output sample. The resulting longer FIR (finite impulseresponse) filters, however, require more computation, which can increasecosts or restrict throughput (output pixels per second generated).

It is possible to create low artifact resampled images, using only foursamples of history in each axis. The cubic model is obtained from thegradients (e.g., g(0) and g(1)) of the sampled signal co-sitedrespectively with the inner two of the four input samples (e.g., f(−1),f(0), f(1), f(2)). The gradients are calculated from the weighted sum ofneighboring input samples. Co-sited refers to two corresponding valuesthat have the same index or are at the same position. For example, f(0)can be co-sited with g(0) (gradient g(0) is calculated using f(0) andits neighboring input samples), and similarly, f(1) can be co-sited withg(1)). The four known values (e.g., f(0), g(0), f(1), g(1)) are thenused to obtain the cubic coefficients. The two inner input samples willsurround the generated output sample, and the gradients each correspondto one of the two corresponding inner input samples. From the estimatedgradients, a model is created which is then used to calculate the outputvalues of the resized image at a fractional position.

Up-sampling (i.e., enlarging) images also attempts to sharpen thoseimages in order to make up for deficiencies in the high frequencyresponses of up-sampling filters. When sharpening an image, maintainingzone-plate frequency response and transition quality is important. Theproposed four-sample approach above may encounter difficulty inmaintaining image sharpness if the four sample points provided introducea more complex feature (e.g., the four points do not represent a singlesine wave).

In view of the foregoing, it would be desirable to provide an economicalapproach for effectively detecting and suppressing ringing artifacts inan image resizing process.

It would also be desirable to provide improved image sharpening whenup-sampling an image in an image resizing process.

SUMMARY OF THE INVENTION

It is an object of this invention to provide an economical approach foreffectively detecting and suppressing ringing artifacts in an imageresizing process.

It is another object of the invention to provide improved imagesharpening when up-sampling an image in an image resizing process.

In accordance with this invention, an image resizer is provided thatincludes the following stages: stage one is gamma modification, whichinvolves removing video gamma correction that was previously applied toan image and reapplying a new gamma. Gamma is applied to a linearluminance system as a power of the luminance value. In a gamma-correctedluminance domain (Y′), narrow black features are linear (or additive) ona white background for a gamma γ equal to about 2.5. In a linearluminance domain (Y), narrow white features are linear (or additive) ona black background for a gamma γ equal to about 1. The two domains Y andY′ are related by Y=(Y′)^(γ) or Y′=(Y)^(1/γ). Although an image mayalready have gamma correction (Y′) applied to it, decimating such animage may create undesirable effects on extreme narrow light impulses.On the other hand, removing the gamma (to work in the Y domain) maycreate undesirable effects on extreme narrow dark impulses. As acompromise, a γ of about 1.6 is preferably applied to the signal priorto resizing (e.g., Y_(1.6)=(Y′)^(γ/1.6)), because it averages theundesirable effects of the narrow light and narrow dark features of theluminance domains.

Stage two involves filtering the inner two of four gamma-modified inputsignal samples. Decimation filtering is applied to the four inputsamples using preferably a {¼, ½, ¼} symmetric 3-tap FIR filter. Becauseeach output value requires three input samples and there are four inputsamples, only the two inner samples are filtered. Decimation by factorsgreater than two can be accomplished by multiple passes ofdecimation-by-two with a final pass of decimation by two or close totwo. Because a final pass may use a decimation factor between one andtwo, a number of selectable filter banks that range fromdecimation-by-one to decimation-by-two are provided in memory. If thedesired filter bank is not available, the final decimation pass uses theclosest available decimation filter. However, this may result innoticeable filter switching during dynamic zooming. Generally, havingmore selectable filter banks results in less noticeable filterswitching.

Stage three involves finding the co-sited gradients. Gradients arecalculated using the input samples, so stage two and stage three can beexecuted in parallel. The gradients are calculated using digitaldifferentiating filters. For image reduction (i.e., down-sampling), asimple pair of differentiating filters is used for gradient estimation.For image enlargement (i.e., up-sampling), an asymmetric FIRdifferentiating filter is advantageously used for gradient estimation inaccordance with the present invention. This filter results in betterimage resizing than conventional 3-tap symmetric filters often used forgradient estimation. The asymmetric FIR filter emphasizes accurate edgehandling over accurate peak handling, resulting in improved zone-platetest signals and a sharper image appearance in general.

Stage four involves calculating the cubic polynomial coefficients usingthe two inner input samples from stage two and the two co-sitedgradients calculated in stage three.

Stage five involves calculating the re-sampled output sample value. Apiece-wise cubic model is preferably generated to obtain a piece-wisecontinuous model of the output signal. The model is then evaluated atthe desired fractional position to obtain a re-sampled value.

Stage six involves gamma modification to reapply gamma correction. Thecompromise gamma correction applied in stage one is removed and theinitial gamma correction is reapplied to produce the resulting resizedimage. The initial gamma needs to be re-applied in order to display theresulting image correctly on a non-linear device, such as a monitor.Reapplying the original gamma correction to the resized image means thatthe system does not change the presentation of flat regions in an image(static component), but affects the position of edges (dynamiccomponent) so that narrow light and dark regions are both preserved fromthe gamma correction applied in stage one.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects and advantages of the invention will beapparent upon consideration of the following detailed description, takenin conjunction with the accompanying drawings, in which like referencecharacters refer to like parts throughout, and in which:

FIG. 1 is a flow chart of an exemplary embodiment of a resizer algorithmaccording to the present invention;

FIG. 2 is an illustration of blended anti-alias filter response curvesaccording to the present invention;

FIG. 3 is a flow chart of an exemplary embodiment of the gradientcalculations of FIG. 1 according to the present invention;

FIG. 4 is a flow chart of an exemplary embodiment of a ratio calculationbased on input signal samples according to the present invention;

FIG. 5 is a flow chart of an exemplary embodiment of detecting andsuppressing ringing artifacts according to the present invention; and

FIG. 6 is an illustration of differentiating filter response curvesaccording to the present invention.

FIG. 7 is a simplified block diagram of an apparatus for detecting andsuppressing ringing artifacts according to the present invention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides economical methods of image resizing thatincludes detection and suppression of ringing artifacts, imagedown-sampling when ringing artifacts are not detected, and determiningwhether an image should be emphasized or unemphasized when up-sampling.

FIG. 1 illustrates a compact image resizer algorithm 10 in accordancewith the present invention. Algorithm 10 begins at step 12 with animage. At step 14, gamma modification occurs. Gamma modificationincludes removing any existing video gamma correction and applying a newselectable gamma as now described.

Image resizing involves a linear process (e.g., re-sampling andfiltering are both linear) in which signals or samples add linearly. Agoal of re-sampling and filtering is to preserve the luminance of animage when decimating. Because re-sampling and filtering are bothlinear, they impose linearity on the re-sampled transitions after eachdecimation pass. Two domains need to be considered: the gamma-correctedluminance (or Y′) domain and the linear luminance (or Y) domain. Linearluminance, or linear intensity, is related to the human perception ofadditive light on a dark background (e.g., the human eye is unable todistinguish between two white spots that are close together with similarresolutions against a black background; the eye discerns the two whitespots as one white spot with additive lightness). The Y′ domain givesgood linearity for fine black detail since darkness is additive in theY′ domain (e.g., two black spots that are close together with similarresolutions against a white background are discerned as one spot withadditive darkness).

The gamma of the input signal may differ from the gamma that producesoptimal results. In that case, the input signal gamma is removed and apreferred processing gamma is applied. Powers of 1/1.6, 1.6, and 2.5 arepreferred values for gamma. In particular, a gamma of about 1.6 isrecommended for the optimal handling of both narrow light and narrowdark features. Once the resampled output sample values have beencalculated, the corresponding reciprocal powers are preferred for gammare-application.

After gamma modification, decimation filtering occurs at step 16. Atthis stage, a {¼, ½, ¼} symmetric 3-tap FIR filter is preferably used tofilter the image.

The filter tap symmetry and its asymmetric response around an angularfrequency of π/2 produce reasonably good results when decimating by two.Furthermore, the 3-tap coefficients {¼, ½, ¼} are economical toimplement in hardware and software because the coefficients are powersof two.

A {¼, ½, ¼} symmetric 3-tap filter is preferably applied to a four-inputsample window, generating two output samples co-sited with the inner twoinput samples. Each of the output samples relates to a correspondinginner input sample. With only four input samples, additional outputsamples cannot be used in later calculations without introducingundesirable artifacts. Decimation by factors greater than two includesone or more passes of decimation-by-two, followed by a final pass ofdecimation by less than two. Decimating by more than two in one pass mayresult in undesirable features such as narrow pass-bands, steeptransition bands, or inadequate filter performance because of the fewtaps. In a final decimation pass, varying response curves may result,causing noticeable filter switching when dynamically changing resizingratios (i.e., dynamic resizing). To prevent filter switching frombecoming too noticeable, multiple selectable pre-specified filter banksare preferably available for the final decimation pass. To obtainsubstantially imperceptible switching over the final decimation pass,nine different filters are preferably provided (i.e., decimation-by-two,decimation-by-one, and seven intermediate blends). This represents acompromise between noticeable filter switching and the amount ofhardware needed to store banks of pre-specified filters. The unblendeddecimation-by-two anti-aliasing filter has the following response:A[ω]=(1+cos(ω))/2  (1)where ω is the angular frequency and A[ω] is the frequency response.

FIG. 2 illustrates {¼, ½, ¼} filter response curves blended with the {0,1, 0} all pass filter anti-alias filter response curves fromdecimation-by-two to decimation-by-one (i.e., no decimation), withcorresponding ideal anti-alias cutoffs. The x-axis represents theangular frequency and the y-axis represents the frequency response.Curve 32 shows an unblended decimation-by-two filter response. Curves34, 36, and 38 are three intermediate blended curves with decimationfactors between one and two. Curve 34 has a decimation factor less thantwo and has a larger decimation factor than curve 36, which has a largerdecimation factor than curve 38. Horizontal line 40 represents theall-pass case for no decimation. Also shown are corresponding ideal, butunrealizable, anti-alias responses with vertical cutoff transitions 42,44, 46, and 48. Moving from left to right, cutoff transition 42corresponds to the decimation-by-two response. Intermediate cutofftransitions 44, 46, and 48 correspond to the intermediate blends. Forthe all-pass case, there is no cutoff frequency. When the angularfrequency is π, the cutoff no longer exists. The selection of filterblending on each resizer pass may be implemented using software.

Blending the {¼, ½, ¼} with the {0, 1, 0} filter is a reasonablecompromise between cost and image quality. Compared to the idealresponses, the filter blending curves may introduce some blurring andaliasing artifacts for decimation ratios between one and two. Becauseblending occurs mainly in the final decimation pass, its impact on theresulting image can be reduced. Other decimation filters may producebetter spectral results, but with more hardware.

Step 18 of algorithm 10 involves gradient calculations. To generateaccurate piece-wise cubic models, gradients co-sited with the originalsample values are needed. The 3-tap filter used for decimationprocessing outputs two samples, which are insufficient to obtainreliably accurate gradients at high frequencies. As a result, gradientsare calculated directly from the input data. Because the gradientcalculations are not dependent on any output data from decimationfiltering, the gradient calculations at step 18 may be performedsimultaneously with the decimation filtering at step 16.

FIG. 3 illustrates an exemplary embodiment of gradient calculationsperformed at step 18. Process 50 begins at step 52 with four adjacentinput samples, which are sufficient to represent a single sine wave ofangular frequency ω. At step 54, the signal can be represented asfollows:ratio=1+2 cos(ω)=(f ₂ −f ⁻¹)/(f ₁ −f ₀),  (2)where f⁻¹, f₀, f₁, and f₂ represent the four adjacent input samples. Thepresence of ringing artifacts is determined at step 56 by examining theratio in equation (2). Because the range of cos(ω) is limited to valuesbetween −1 and +1, the ratio ideally ranges from −1 to +3. A ratio thatexceeds +3 or that equals zero indicates the presence of visible ringingartifacts.

In accordance with the present invention, however, no division inequation (2) is required to determine whether ringing artifacts arepresent. Performing division can be costly to implement. Instead, theratio in equation (2) can be examined for a fixed ω by first multiplyingthrough the denominator (f₁−f₀) and then by making cost effectivecomparisons.

FIG. 4 illustrates an exemplary embodiment of the ratio calculationperformed at step 54 of FIG. 3. Initially, at step 72, the differencebetween the two inner adjacent input samples is calculated(slope1=f1−f0). Similarly, at step 74, the difference between the twoouter input samples is calculated (step=f2−f−1). Once these two values(slope1 and step) have been calculated, a test is performed at step 76to determine if the value of slope1 is less than the value of zero. Ifslope1 is less than zero, the ratio is normalized at step 78.Normalization is performed by multiplying both the denominator andnumerator of equation (2) (slope1 and step, respectively) by negativeone (slope_denom=(−1)*slope1 and slope_num=(−1)*step). If slope1 is notless than zero, then slope1 and step are assigned the values of thedenominator and numerator (slope_denom=slope1 and slope_num=step),respectively, at step 79.

Note that when slope1 is equal to zero (i.e., f₁=f₀), the ratio resultsin an undirected infinity. This is common in text images where adjacentvalues are identical. When this occurs, a unique single sine-wavefrequency cannot be found that goes through the four points f₂, f₁, f₀,and f⁻¹. Although f₁ equaling f₀ can occasionally occur in a single sinewave, this case is interpreted as requiring ringing suppression. Ringingsuppression, using linear interpolation, will join f₁ to f₀ with a flatline. Thus, an undirected infinity, which may or may not cause minorringing artifacts, will result in suppression.

FIG. 5 illustrates an exemplary embodiment of detecting and suppressingringing artifacts as performed at steps 56 and 58, respectively, of FIG.3. To determine whether ringing artifacts exist, the two conditions forringing are tested at step 82. The first condition is whether the ratioof the input signal exceeds a value of positive three(slope_num>3*slope_denom), which indicates that no real value of ω ispossible. This situation may occur as a result of adjacent sharp edgesin an image. The second condition is whether the ratio of the inputsignals results in an undirected infinity (slope_denom=0), as describedabove.

If either condition at step 82 exists, ringing is substantiallysuppressed at step 86 using a linear interpolation model. This isaccomplished, at step 85, by setting the two gradient values (gr0 andgr1) equal to slope1, which is the difference between the inner inputsamples. The gradients are set to the inner slope because waywardsamples further out may contribute to ringing if they are involved inthe gradient calculations (step 18 of FIG. 1).

If neither of the two conditions tested at step 82 exist, process 80ends at step 84 where no ringing suppression is performed.

A condition may also exist where equation (2) equals exactly three(e.g., a linear ramp). This value falls just outside the scope of theconditions defined above for ringing. However, a model (described below)fit to the four samples using a differentiating filter results in thesame gradient values indicated for suppression of ringing in the twoconditions above. This shows that the algorithm for detecting andsuppressing ringing has continuity around the decision threshold, thusallowing for noise.

FIG. 7 is a simplified block diagram of an apparatus for detecting andsuppressing ringing artifacts according to the present invention. Theapparatus includes a processor 700 configured to perform the steps inFIGS. 4 and 5 described above.

Referring to FIG. 3, if ringing is not detected, process 50 continues tostep 60 to determine whether down-sampling (i.e., reducing the imagesize) or up-sampling (i.e., enlarging the image size) is beingperformed. If down-sampling, a simple pair of differentiating filters isused at step 62 to estimate gradients. This allows edges to be enhanced.Gradients are calculated as follows:gr0=(f ₁ −f ⁻¹)/2gr1=(f ₂ −f ₀)/2  (3)The angular frequency response of the simple pair of differentiatingfilters is G[107 ]=sin(ω), which roughly matches the idealdifferentiator convolved with the filter response of the anti-aliasingfilter from equation (1). The ideal differentiating response isrepresented by:D[ω]=[ω(1+cos(ω))]/2  (4)

FIG. 6 illustrates a pair of differentiating filter response curves.Curve 92 shows the ideal differentiating response represented byequation (4). Curve 94 shows the actual differentiating filter responseused for gradient estimation. The actual response is close to the idealresponse for angular frequencies below π/4. At higher angularfrequencies, the discrepancy in the two curves results in someartifacts. Because the filters do not cut off all frequencies above π/2,aliasing may still occur although it is advantageously significantlyreduced.

When down-sampling, prior artificial model sharpening of any kind mayincrease visible aliasing artifacts. Artificial model sharpeningincreases spectral energy which may alias back into the passband whendown-sampling. Thus, since sharpening occurs naturally fromdown-sampling, providing artificial sharpening when down-sampling isunnecessary.

Up-sampling attempts to sharpen images without affecting zone-platefrequency response and transition quality. To up-sample an image,process 50 of FIG. 3 moves to step 64. For cases where the angularfrequency is between zero and π, a single sine wave may be fitted to thefour sample points to interpolate between the inner two points. However,this approach is expensive. A less costly alternative is to useasymmetric FIR differentiating filters in accordance with the presentinvention. Two such filters preferably used are as follows:filt0=−(3/2)f ⁻¹ +f ₀ +f ₁−(1/2)f ₂filt1=(1/2)f ⁻¹ −f ₀ −f ₁+(3/2)f ₂  (5)These filters represent a compromise between their anti-symmetricresponse and their symmetric response. An anti-symmetric response hasgood edge accuracy while a symmetric response has good peak accuracy.Because the human eye is more discerning of edge accuracy than peakaccuracy, the filters are designed to produce a better anti-symmetricedge response while sacrificing some of the symmetric peak response.

Once the outputs of the filters at step 64 have been determined, process50 moves to step 66 where the ratio of step 54, equation (2), is againexamined. If the ratio is between −1 and +3, the outputs of filtersfilt0 and filt1 are unemphasized at step 68. Because up-samplingtypically sharpens an image, the filter outputs from step 64, equation(5), are each attenuated by ⅝ to obtain an accurate unemphasizeddifferentiating response for the single sine wave case. Unemphasizing animage does not change the sharpness of the image. The new unemphasizedgradients are:gr0=(5/8)filt0gr1=(5/8)filt1  (6)The frequency response of the unemphasized filter outputs provides anear ideal differentiating response. However, if the ratio is less than−1, the four sample points do not represent a single sine wave, but amulti-sine wave. Because the multiple adjacent edges of a multi-sinewave can cause ringing artifacts, an image is preferably emphasized(i.e., sharpened) to make the desired transition more prominent. Anemphasized image is unattenuated. At step 69, the filter outputs fromstep 64, equation (5), become the emphasized gradients as follows:gr0=filt0gr1=filt1  (7)

Emphasizing and unemphasizing filter outputs filt0 and filt1 preferablyresult in images that appear very much like the original image.

The use of short asymmetric FIR filters in up-sampling results in betterimage resizing than the more conventional anti-symmetric differentiatingfilters. The asymmetric filters provide good edge and extended-frequencyresponses with narrow peak sharpening characteristics. By emphasizingaccurate edge handling where gradients are steepest and by sacrificingsome spectral performance on peaks where gradients are shallowest,improved zone-plate test signal results are obtained. Resized images areof high quality when viewed by the human eye.

Turning back to FIG. 1, upon calculation of gradients, algorithm 10moves to step 20 and step 22 to calculate the cubic model coefficientsand the re-sampled output values, respectively. To resample the imageonce the sample data and co-sited gradients are found, piece-wisecontinuous models of the signal are preferably generated independentlyalong each axis. The piece-wise cubic model may be obtained as follows:

$\begin{matrix}{{f\left( {\Delta\; p} \right)} = {\sum\limits_{i = 0}^{3}{C_{i}\left( {\Delta\; p} \right)}^{i}}} & (8)\end{matrix}$where 0≦Δp≦1. The coefficients C_(i) may be found as follows:

$\begin{matrix}\begin{matrix}{k = {{f1} - {f0}}} \\{C_{3} = {{{gr}\; 1} + {{gr}\; 0} - {2\; k}}} \\{C_{2} = {k - C_{3} - {{gr}\; 0}}} \\{C_{1} = {{gr}\; 0}} \\{C_{0} = {f0}}\end{matrix} & (9)\end{matrix}$where f0 and f1 are the original two inner input samples surrounding theoutput sample, and gr0 and gr1 are their corresponding co-sitedgradients.

Lastly, at step 24, gamma restoration undoes the gamma modification ofstep 14, restoring the original gamma. The resulting image is suitablefor display on a monitor. Algorithm 10 ends at step 26.

Testing of up-sampled images using resizer algorithm 10 have shownre-sampled images to be as sharp as or sharper than those obtained fromadvanced commercial software packages that have the option of using moresample support. While performance on zone-plate test signals near theNyquist limit may be weak compared to other image resizing software,this has little bearing on visual quality in general.

Thus it is seen that an economical approach to detection and suppressionof ringing artifacts and improved image sharpening when up-sampling isprovided. One skilled in the art will appreciate that the presentinvention can be practiced by other than the described embodiments,which are presented for purposes of illustration and not of limitation,and the present invention is limited only by the claims which follow.

1. A method of suppressing ringing artifacts during digital resizing of an image, said method comprising: calculating a first difference between two inner of four adjacent image samples; calculating a second difference between two outer of said four samples; correcting said first and second differences by inverting the sign of said first and second differences when said first difference is negative; tripling said first corrected difference; comparing said second corrected difference with said tripled first corrected difference; and suppressing ringing artifacts between said two inner samples using a linear interpolation model when said second corrected difference is greater than said tripled first corrected difference.
 2. The method of claim 1 further comprising suppressing ringing artifacts between said two inner samples using a linear interpolation model when said first difference is zero.
 3. The method of claim 2 wherein each said suppressing of ringing artifacts occurs independently in each axis in a two dimensional image.
 4. The method of claim 2 further comprising using an interpolation model with an emphasized frequency response characteristic with said two inner samples when said second corrected difference is less than the negative of said first corrected difference.
 5. The method of claim 4 wherein said interpolation model comprises cubic polynomial models.
 6. A method of detecting ringing artifacts during digital resizing of an image, said method comprising: calculating a first difference between two inner of four adjacent image samples; calculating a second difference between two outer of said four samples; comparing said first difference with zero; tripling said first difference; and comparing said second difference with said tripled first difference, wherein: said ringing artifacts are detected when either said first difference equals zero or said second difference is greater than said tripled first difference.
 7. A method of suppressing ringing artifacts during digital image resizing, said method comprising: calculating a difference between two inner of four adjacent image samples; setting a first gradient equal to said difference; setting a second gradient equal to said difference; and suppressing ringing artifacts using said first and second gradients to generate a continuous signal model of an image being resized.
 8. The method of claim 7 wherein said first and second gradients are used to calculate coefficients of said continuous signal model.
 9. Apparatus for suppressing ringing artifacts during digital resizing of an image, said apparatus comprising: means for calculating a first difference between two inner of four adjacent image samples; means for calculating a second difference between two outer of said four samples; means for correcting said first and second differences by inverting the sign of said first and second differences when said first difference is negative; means for tripling said first corrected difference; means for comparing said second corrected difference with said tripled first corrected difference; and means for suppressing ringing artifacts between said two inner samples using a linear interpolation model when said second corrected difference is greater than said tripled first corrected difference.
 10. The appar atus of claim 9 further comprising suppressing ringing artifacts between said two inner samples using a linear interpolation model when said first difference is zero.
 11. The apparatus of claim 10 wherein each said suppressing of ringing artifacts occurs independently in each axis in a two dimensional image.
 12. The apparatus of claim 10 further comprising using an interpolation model with an emphasized frequency response characteristic with said two inner samples when said second corrected difference is less than the negative of said first corrected difference.
 13. The apparatus of claim 12 wherein said interpolation model comprises cubic polynomial models.
 14. Apparatus for detecting ringing artifacts during digital resizing of an image, said apparatus comprising: means for calculating a first difference between two inner of four adjacent image samples; means for calculating a second difference between two outer of said four samples; means for comparing said first difference with zero; means for tripling said first difference; and means for comparing said second difference with said tripled first difference, wherein: said ringing artifacts are detected when either said first difference equals zero or said second difference is greater than said tripled first difference.
 15. Apparatus for suppressing ringing artifacts during digital image resizing, said apparatus comprising: means for calculating a difference between two inner of four adjacent image samples; means for setting a first gradient equal to said difference; means for setting a second gradient equal to said difference; and means for suppressing ringing artifacts using said first and second gradients to generate a continuous signal model of an image being resized.
 16. The apparatus of claim 15 wherein said first and second gradients are used to calculate coefficients of said continuous signal model. 